python

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Best practices for Python development including PEP 8, modern Python 3.10+ features, and package management.

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[Code Review]

When & Why to Use This Skill

This Claude skill optimizes Python development workflows by enforcing PEP 8 standards, modern Python 3.10+ syntax, and robust package management. It ensures high-quality code production through type hinting, efficient error handling, and standardized testing practices, making it an essential tool for building scalable and maintainable software.

Use Cases

  • Modernizing Legacy Code: Automatically refactor older Python scripts to utilize modern features like f-strings, pathlib, and match-case statements for improved performance and readability.
  • Project Initialization & Dependency Management: Guide the setup of new Python environments using pyproject.toml and virtual environments to ensure consistent and reproducible builds.
  • Automated Quality Assurance: Perform comprehensive code reviews to identify PEP 8 violations, improper error handling, and missing type hints to maintain a clean codebase.
  • Testing Framework Implementation: Assist in designing and writing high-coverage test suites using pytest and fixtures to ensure software reliability and prevent regressions.
namepython
descriptionBest practices for Python development including PEP 8, modern Python 3.10+ features, and package management.
globs["**/*.py", "**/Pipfile", "**/pyproject.toml", "**/setup.py", "**/requirements.txt"]
priority100
tags["language"]

Python Best Practices

Code Style

  • Follow PEP 8 style guide
  • Use snake_case for functions/variables
  • Use PascalCase for classes
  • Maximum line length: 88 (Black) or 79 (PEP 8)
  • Use meaningful variable names

Modern Python (3.10+)

  • Use f-strings for formatting: f"Hello {name}"
  • Use pathlib for file paths instead of os.path
  • Use dataclasses or Pydantic for data models
  • Type hints for function signatures
  • Use walrus operator := for assignment expressions
  • Use match-case for pattern matching
  • Use | for union types instead of Union

Error Handling

  • Be specific with exception types
  • Use context managers (with) for resources
  • Log exceptions with traceback
  • Never use bare except:
  • Use finally for cleanup

Package Management

  • Always use virtual environments (venv, conda, poetry)
  • Pin dependency versions in requirements.txt
  • Use pyproject.toml for modern projects
  • Separate dev dependencies from production

Testing

  • Use pytest over unittest
  • Use fixtures for test setup
  • Use parametrize for multiple test cases
  • Aim for high test coverage